Fast Second Degree Total Variation Method for Image Compressive Sensing

This paper presents a computationally efficient algorithm for image compressive sensing reconstruction using a second degree total variation (HDTV2) regularization. Firstly, a preferably equivalent formulation of the HDTV2 functional is derived, which can be formulated as a weighted L ₁-L ₂ mixed norm of second degree image derivatives under the spectral decomposition framework. Secondly, using the equivalent formulation of HDTV2, we introduce an efficient forward-backward splitting (FBS) scheme to solve the HDTV2-based image reconstruction model. Furthermore, from the averaged non-expansive operator point of view, we make a detailed analysis on the convergence of the proposed FBS algorithm. Experiments on medical images demonstrate that the proposed method outperforms several fast algorithms of the TV and HDTV2 reconstruction models in terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM) and convergence speed.     

A Reconstruction Method Based on AL0FGD for Compressed Sensing in Border Monitoring WSN System

In this paper, to monitor the border in real-time with high efficiency and accuracy, we applied the compressed sensing (CS) technology on the border monitoring wireless sensor network (WSN) system and proposed a reconstruction method based on approximately l₀ norm and fast gradient descent (AL0FGD) for CS. In the frontend of the system, the measurement matrix was used to sense the border information in a compressed manner, and then the proposed reconstruction method was applied to recover the border information at the monitoring terminal. To evaluate the performance of the proposed method, the helicopter sound signal was used as an example in the experimental simulation, and three other typical reconstruction algorithms 1)split Bregman algorithm, 2)iterative shrinkage algorithm, and 3)smoothed approximate l₀ norm (SL0), were employed for comparison. The experimental results showed that the proposed method has a better performance in recovering the helicopter sound signal in most cases, which could be used as a basis for further study of the border monitoring WSN system.     

Estimation of Noise-Free Variance to Measure Heterogeneity

Variance is a statistical parameter used to characterize heterogeneity or variability in data sets. However, measurements commonly include noise, as random errors superimposed to the actual value, which may substantially increase the variance compared to a noise-free data set. Our aim was to develop and validate a method to estimate noise-free spatial heterogeneity of pulmonary perfusion using dynamic positron emission tomography (PET) scans. On theoretical grounds, we demonstrate a linear relationship between the total variance of a data set derived from averages of n multiple measurements, and the reciprocal of n. Using multiple measurements with varying n yields estimates of the linear relationship including the noise-free variance as the constant parameter. In PET images, n is proportional to the number of registered decay events, and the variance of the image is typically normalized by the square of its mean value yielding a coefficient of variation squared (CV ²). The method was evaluated with a Jaszczak phantom as reference spatial heterogeneity (CV_r ²) for comparison with our estimate of noise-free or ‘true’ heterogeneity (CV _t ²). We found that CV _t ² was only 5.4% higher than CV _r ². Additional evaluations were conducted on 38 PET scans of pulmonary perfusion using ¹³NN-saline injection. The mean CV _t ² was 0.10 (range: 0.03–0.30), while the mean CV ² including noise was 0.24 (range: 0.10–0.59). CV _t ² was in average 41.5% of the CV ² measured including noise (range: 17.8–71.2%). The reproducibility of CV _t ² was evaluated using three repeated PET scans from five subjects. Individual CV _t ² were within 16% of each subject''s mean and paired t-tests revealed no difference among the results from the three consecutive PET scans. In conclusion, our method provides reliable noise-free estimates of CV _t ² in PET scans, and may be useful for similar statistical problems in experimental data.     

A Virtual Clinical Trial of FDG-PET Imaging of Breast Cancer: Effect of Variability on Response Assessment

INTRODUCTION: There is growing interest in using positron emission tomography (PET) standardized uptake values (SUVs) to assess tumor response to therapy. However, many error sources compromise the ability to detect SUV changes. We explore relationships between these errors and overall SUV variability. METHODS: We used simulations in a virtual clinical trial framework to study impacts of error sources from scanning and analysis effects on assessment of SUV changes. We varied tumor diameter, scan duration, pretherapy SUV, magnitude of change in SUV, image reconstruction filter, and SUV metric. Poisson noise was added to the raw data before image reconstruction. Variance from global sources of error, e.g., scanner calibration, was incorporated. Two thousand independent noisy sinograms per scenario were generated and reconstructed. We used SUVs to create receiver operating characteristic (ROC) curves to quantify ability to assess response. Integrating area under the ROC curve summarized ability to detect SUV changes. RESULTS: Scan duration and image reconstruction method had relatively little impact on ability to measure response. SUV_MAX is nearly as effective as SUV_MEAN, especially with increased image smoothing and despite size-matched region of interest placement. For an effective variability of 15%, we found the Positron Emission Tomography Response Criteria in Solid Tumors criteria for measuring response (±30%) similar to the European Organization for Research and Treatment of Cancer criteria (±25%). CONCLUSIONS: For typical PET variance levels, tumor response must be 30% to 40% to be reliably determined using SUVs. PET scan duration and image reconstruction method had relatively little effect.     

Automated Movement Correction for Dynamic PET/CT Images: Evaluation with Phantom and Patient Data

Head movement during a dynamic brain PET/CT imaging results in mismatch between CT and dynamic PET images. It can cause artifacts in CT-based attenuation corrected PET images, thus affecting both the qualitative and quantitative aspects of the dynamic PET images and the derived parametric images. In this study, we developed an automated retrospective image-based movement correction (MC) procedure. The MC method first registered the CT image to each dynamic PET frames, then re-reconstructed the PET frames with CT-based attenuation correction, and finally re-aligned all the PET frames to the same position. We evaluated the MC method''s performance on the Hoffman phantom and dynamic FDDNP and FDG PET/CT images of patients with neurodegenerative disease or with poor compliance. Dynamic FDDNP PET/CT images (65 min) were obtained from 12 patients and dynamic FDG PET/CT images (60 min) were obtained from 6 patients. Logan analysis with cerebellum as the reference region was used to generate regional distribution volume ratio (DVR) for FDDNP scan before and after MC. For FDG studies, the image derived input function was used to generate parametric image of FDG uptake constant (K_i) before and after MC. Phantom study showed high accuracy of registration between PET and CT and improved PET images after MC. In patient study, head movement was observed in all subjects, especially in late PET frames with an average displacement of 6.92 mm. The z-direction translation (average maximum = 5.32 mm) and x-axis rotation (average maximum = 5.19 degrees) occurred most frequently. Image artifacts were significantly diminished after MC. There were significant differences (P<0.05) in the FDDNP DVR and FDG Ki values in the parietal and temporal regions after MC. In conclusion, MC applied to dynamic brain FDDNP and FDG PET/CT scans could improve the qualitative and quantitative aspects of images of both tracers.     

Robustness of post-reconstruction and direct kinetic parameter estimates under rigid head motion in dynamic brain PET imaging

ObjectiveDynamic PET imaging is extensively used in brain imaging to estimate parametric maps. Inter-frame motion can substantially disrupt the voxel-wise time-activity curves (TACs), leading to erroneous maps during kinetic modelling. Therefore, it is important to characterize the robustness of kinetic parameters under various motion and kinetic model related factors.MethodsFully 4D brain simulations ([¹⁵O]H₂O and [¹⁸F]FDG dynamic datasets) were performed using a variety of clinically observed motion patterns. Increasing levels of head motion were investigated as well as varying temporal frames of motion initiation. Kinetic parameter estimation was performed using both post-reconstruction kinetic analysis and direct 4D image reconstruction to assess bias from inter-frame emission blurring and emission/attenuation mismatch.ResultsKinetic parameter bias heavily depends on the time point of motion initiation. Motion initiated towards the end of the scan results in the most biased parameters. For the [¹⁸F]FDG data, k₄ is the more sensitive parameter to positional changes, while K₁ and blood volume were proven to be relatively robust to motion. Direct 4D image reconstruction appeared more sensitive to changes in TACs due to motion, with parameter bias spatially propagating and depending on the level of motion.ConclusionKinetic parameter bias highly depends upon the time frame at which motion occurred, with late frame motion-induced TAC discontinuities resulting in the least accurate parameters. This is of importance during prolonged data acquisition as is often the case in neuro-receptor imaging studies. In the absence of a motion correction, use of TOF information within 4D image reconstruction could limit the error propagation.     

Curve fitting approach for measurement of cellular osmotic properties by the electrical sensing zone method. II. Membrane water permeability

In a companion paper, we demonstrated that dynamic range limitations can confound measurement of the osmotically inactive volume using electrical sensing zone instruments (e.g., Coulter counters), and presented an improved parameter estimation method in which a lognormal function was fit to the cell volume distribution to allow extrapolation beyond the bounds of the data. Presently, we have investigated the effect of dynamic range limitations on measurement of the cell membrane water permeability (L_p), and adapted the lognormal extrapolation method for estimation of L_p from transient volume data. An alternative strategy (the volume limit adjustment method, in which the measured isotonic volume distribution is used to generate model predictions for curve fitting, and the bounds of the dynamic range are adjusted such that extrapolation is not required) was also developed. The performance of these new algorithms was compared to that of a conventional parameter estimation method. The best-fit L_p values from in vitro experiments with mouse insulinoma (MIN6) cells differed significantly for the different parameter estimation techniques (p < 0.001). Using in silico experiments, the volume limit adjustment method was shown to be the most accurate (relative error 0.4 ± 3.2%), whereas the conventional method underestimated L_p by 19 ± 2% for MIN6 cells. Parametric analysis revealed that the error associated with the conventional method was sensitive to the dynamic range and the width of the volume distribution. Our initial implementation of the lognormal extrapolation method also yielded significant errors, whereas accuracy of this algorithm improved after including a normalization scheme.     

Production of extracellular PETase from Ideonella sakaiensis using sec-dependent signal peptides in E. coli

Poly(ethylene terephthalate) (PET) is the most commonly used polyester polymer resin in fabrics and storage materials, and its accumulation in the environment is a global problem. The ability of PET hydrolase from Ideonella sakaiensis 201-F6 (IsPETase) to degrade PET at moderate temperatures has been studied extensively. However, due to its low structural stability and solubility, it is difficult to apply standard laboratory-level IsPETase expression and purification procedures in industry. To overcome this difficulty, the expression of IsPETase can be improved by using a secretion system. This is the first report on the production of an extracellular IsPETase, active against PET film, using Sec-dependent translocation signal peptides from E. coli. In this work, we tested the effects of fusions of the Sec-dependent and SRP-dependent signal peptides from E. coli secretory proteins into IsPETase, and successfully produced the extracellular enzyme using pET22b-SP_MalE:IsPETase and pET22b-SP_LamB:IsPETase expression systems. We also confirmed that the secreted IsPETase has PET-degradation activity. The work will be used for development of a new E. coli strain capable of degrading and assimilating PET in its culture medium.     

Prediction of time-integrated activity coefficients in PRRT using simulated dynamic PET and a pharmacokinetic model

PurposeTo investigate the accuracy of predicted time-integrated activity coefficients (TIACs) in peptide-receptor radionuclide therapy (PRRT) using simulated dynamic PET data and a physiologically based pharmacokinetic (PBPK) model.MethodsPBPK parameters were estimated using biokinetic data of 15 patients after injection of (152 ± 15) MBq of ¹¹¹In-DTPAOC (total peptide amount (5.78 ± 0.25) nmol). True mathematical phantoms of patients (MPPs) were the PBPK model with the estimated parameters. Dynamic PET measurements were simulated as being done after bolus injection of 150 MBq ⁶⁸Ga-DOTATATE using the true MPPs. Dynamic PET scans around 35 min p.i. (P₁), 4 h p.i. (P₂) and the combination of P₁ and P₂ (P₃) were simulated. Each measurement was simulated with four frames of 5 min each and 2 bed positions. PBPK parameters were fitted to the PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 5.1 GBq of ⁹⁰Y-DOTATATE over 30 min in both true and PET-predicted MPPs. TIACs of simulated therapy were calculated, true MPPs (true TIACs) and predicted MPPs (predicted TIACs) followed by the calculation of variabilities v.ResultsFor P₁ and P₂ the population variabilities of kidneys, liver and spleen were acceptable (v < 10%). For the tumours and the remainders, the values were large (up to 25%). For P₃, population variabilities for all organs including the remainder further improved, except that of the tumour (v > 10%).ConclusionTreatment planning of PRRT based on dynamic PET data seems possible for the kidneys, liver and spleen using a PBPK model and patient specific information.     

Catalytic mechanism of ancestral L-lysine oxidase assigned by sequence data mining

A large number of protein sequences are registered in public databases such as PubMed. Functionally uncharacterized enzymes are included in these databases, some of which likely have potential for industrial applications. However, assignment of the enzymes remained difficult tasks for now. In this study, we assigned a total of 28 original sequences to uncharacterized enzymes in the FAD-dependent oxidase family expressed in some species of bacteria including Chryseobacterium, Flavobacterium, and Pedobactor. Progenitor sequence of the assigned 28 sequences was generated by ancestral sequence reconstruction, and the generated sequence exhibited L-lysine oxidase activity; thus, we named the enzyme AncLLysO. Crystal structures of ligand-free and ligand-bound forms of AncLLysO were determined, indicating that the enzyme recognizes L-Lys by hydrogen bond formation with R76 and E383. The binding of L-Lys to AncLLysO induced dynamic structural change at a plug loop formed by residues 251 to 254. Biochemical assays of AncLLysO variants revealed the functional importance of these substrate recognition residues and the plug loop. R76A and E383D variants were also observed to lose their activity, and the k_cat/K_m value of G251P and Y253A mutations were approximately 800- to 1800-fold lower than that of AncLLysO, despite the indirect interaction of the substrates with the mutated residues. Taken together, our data demonstrate that combinational approaches to sequence classification from database and ancestral sequence reconstruction may be effective not only to find new enzymes using databases of unknown sequences but also to elucidate their functions.     

Model-based simulation of dynamic magnetic resonance imaging signals

This paper presents a model-based method to efficiently simulate dynamic magnetic resonance imaging signals. Using an analytical spatiotemporal object model, the method can approximate time-varying k-space signals such as those from objects in motion and/or during dynamic contrast enhancement. Both rigid-body and non-rigid-body motions can be simulated using the proposed method. In addition, it can simulate data with arbitrary data sampling order and/or non-uniform k-space trajectory. A set of simulated images were compared with real data acquired from a rat model on a 4.7 T scanner to verify the model. The efficient simulation method is expected to be useful for rapid testing of various imaging and image analysis algorithms such as image reconstruction, image registration, motion compensation, and kinetic parameter mapping.     

In vivo evaluation of [18F]FECIMBI-36, an agonist 5-HT2A/2C receptor PET radioligand in nonhuman primate

We recently reported the radiosynthesis and in vitro evaluation of [¹⁸F]-2-(4-bromo-2,5-dimethoxyphenyl)-N-(2-(2-fluoroethoxy)benzyl)ethanamine, ([¹⁸F]FECIMBI-36) or ([¹⁸F]1), an agonist radioligand for 5HT_2A/2C receptors in postmortem samples of human brain. Herein we describe the in vivo evaluation of [¹⁸F]FECIMBI-36 in vervet/African green monkeys by PET imaging. PET images show that [¹⁸F]FECIMBI-36 penetrates the blood-brain barrier and a low retention of radioactivity is observed in monkey brain. Although the time activity curves indicate a somehow heterogeneous distribution of the radioligand in the brain, the low level of [¹⁸F]FECIMBI-36 in brain may limit the use of this tracer for quantification of 5-HT_2A/2C receptors by PET.     

In vivo evaluation of IGF1R/IR PET ligand [18F]BMS-754807 in rodents

In vivo evaluation of [¹⁸F]BMS-754807 binding in mice and rats using microPET and biodistribution methods is described herein. The radioligand shows consistent binding characteristics, in vivo, in both species. Early time frames of the microPET images and time activity curves of brain indicate poor penetration of the tracer across the blood brain barrier (BBB) in both species. However, microPET experiments in mice and rats show high binding of the radioligand outside the brain to heart, pancreas and muscle, the organs known for higher expression of IGF1R/1R. Biodistribution analysis 2 h after injection of [¹⁸F]BMS-754807 in rats show negligible [¹⁸F]defluorination as reflected by the low bone uptake and clearance from blood. Overall, the data indicate that [¹⁸F]BMS-754807 can potentially be a radiotracer for the quantification of IGF1R/IR outside the brain using PET.     

CT iterative reconstruction in image space: A phantom study

Although iterative reconstruction is widely applied in SPECT/PET, its introduction in clinical CT is quite recent, in the past the demand for extensive computer power and long image reconstruction times have stopped the diffusion of this technique. Recently Iterative Reconstruction in Image Space (IRIS) has been introduced on Siemens top CT scanners. This recon method works on image data area, reducing the time-consuming loops on raw data and noise removal is obtained in subsequent iterative steps with a smoothing process. We evaluated image noise, low contrast resolution, CT number linearity and accuracy, transverse and z-axis spatial resolution using some dedicated phantoms in single, dual source and cardiac mode. We reconstructed images with a traditional filtered back-projection algorithm and with IRIS. The iterative procedure preserves spatial resolution, CT number accuracy and linearity moreover decreases image noise. These preliminary results support the idea that dose reduction with preserved image quality is possible with IRIS, even if studies on patients are necessary to confirm these data.     

Réduction des effets de la respiration en imagerie TEP pulmonaire par une acquisition synchronisée à la respiration associée à une TDM en apnée

Respiratory motion causes a spread of lesion uptake over a larger area in Positron Emission Tomography (PET) images for moving structures. When CT images are used for attenuation correction of emission data, this motion may alter the quantization of PET images. We present the clinical results of a respiratory-gated PET processing “CT-based” method, which aims to improve PET-CT coregistration by using an additional breath-hold CT (BH-CT). The CT-based protocol consisted in a 10-min List Mode respiratory-gated PET acquisition, followed by an end-expiration BH-CT acquisition. During these two examinations, the respiratory signal was recorded continuously. Eleven pulmonary lesions were studied. Patients underwent both a standard clinical PET protocol (free breathing) and the CT-based protocol. The respective performances of the CT-based and clinical PET methods were evaluated by comparing the distances between the lesions’ centroids on PET and CT images. SUV_MAX (Standardized Uptake Value) and volume variations were also investigated. The CT-based method showed a significant reduction (p = 0.049) of centroid distances (mean relative change versus standard method: 49%). We also noted a higher SUV_MAX (mean change: 39%). Lesion volumes were significantly lower (p = 0.026) in CT-based PET volumes (mean change: 43%) compared with standard ones. The CT-based method improves PET-CT coregistration of pulmonary lesions. This protocol should lead to more accurate attenuation correction and thus improve SUV measurement.     

Robust Generalized Low Rank Approximations of Matrices

In recent years, the intrinsic low rank structure of some datasets has been extensively exploited to reduce dimensionality, remove noise and complete the missing entries. As a well-known technique for dimensionality reduction and data compression, Generalized Low Rank Approximations of Matrices (GLRAM) claims its superiority on computation time and compression ratio over the SVD. However, GLRAM is very sensitive to sparse large noise or outliers and its robust version does not have been explored or solved yet. To address this problem, this paper proposes a robust method for GLRAM, named Robust GLRAM (RGLRAM). We first formulate RGLRAM as an l ₁-norm optimization problem which minimizes the l ₁-norm of the approximation errors. Secondly, we apply the technique of Augmented Lagrange Multipliers (ALM) to solve this l ₁-norm minimization problem and derive a corresponding iterative scheme. Then the weak convergence of the proposed algorithm is discussed under mild conditions. Next, we investigate a special case of RGLRAM and extend RGLRAM to a general tensor case. Finally, the extensive experiments on synthetic data show that it is possible for RGLRAM to exactly recover both the low rank and the sparse components while it may be difficult for previous state-of-the-art algorithms. We also discuss three issues on RGLRAM: the sensitivity to initialization, the generalization ability and the relationship between the running time and the size/number of matrices. Moreover, the experimental results on images of faces with large corruptions illustrate that RGLRAM obtains the best denoising and compression performance than other methods.     

Can Imaging Parameters Provide Information Regarding Histopathology in Head and Neck Squamous Cell Carcinoma? A Meta-Analysis

OBJECT: Our purpose was to provide data regarding relationships between different imaging and histopathological parameters in HNSCC. METHODS: MEDLINE library was screened for associations between different imaging parameters and histopathological features in HNSCC up to December 2017. Only papers containing correlation coefficients between different imaging parameters and histopathological findings were acquired for the analysis. RESULTS: Associations between ¹⁸F-FDG positron emission tomography (PET) and KI 67 were reported in 8 studies (236 patients). The pooled correlation coefficient was 0.20 (95% CI = [?0.04; 0.44]). Furthermore, in 4 studies (64 patients), associations between ¹⁸F-fluorothymidine PET and KI 67 were analyzed. The pooled correlation coefficient between SUV_max and KI 67 was 0.28 (95% CI = [?0.06; 0.94]). In 2 studies (23 patients), relationships between KI 67 and dynamic contrast-enhanced magnetic resonance imaging were reported. The pooled correlation coefficient between K_trans and KI 67 was ?0.68 (95% CI = [?0.91; ?0.44]). Two studies (31 patients) investigated correlation between apparent diffusion coefficient (ADC) and KI 67. The pooled correlation coefficient was ?0.61 (95% CI = [?0.84; ?0.38]). In 2 studies (117 patients), relationships between ¹⁸F-FDG PET and p53 were analyzed. The pooled correlation coefficient was 0.0 (95% CI = [?0.87; 0.88]). There were 3 studies (48 patients) that investigated associations between ADC and tumor cell count in HNSCC. The pooled correlation coefficient was ?0.53 (95% CI = [?0.74; ?0.32]). Associations between ¹⁸F-FDG PET and HIF-1α were investigated in 3 studies (72 patients). The pooled correlation coefficient was 0.44 (95% CI = [?0.20; 1.08]). CONCLUSIONS: ADC may predict cell count and proliferation activity, and SUV_max may predict expression of HIF-1α in HNSCC. SUV_max cannot be used as surrogate marker for expression of KI 67 and p53.     

Tetra(β-phenothiazinyl) zinc phthalocyanine: An easily prepared D4-A system for efficient photoinduced electron transfer

Four identical electron donor (D) moieties, phenothiazines (PTZs), were covalently attached onto the same acceptor (A), zinc phthalocyanine (ZnPc), to form ZnPc(β-PTZ)₄, i.e. D₄-A. The symmetrical D₄-A was synthesized by the condensation method to examine intra-molecular photoinduced electron transfer (PET). The common electron donor-spacer-acceptor (D-A) photosynthetic models, which involve the asymmetrical synthesis of a mono-substituted porphyrin or its analogs, suffer from a low yield and arduous isolation, since D-A is only one of several products (D_n-A or A_n-D, n = 0, 2-4). The D₄-A preparation, however, can be carried out without the problem. The steady state and time-resolved fluorescence of the D₄-A were measured and compared with that of ZnPc(β-R)₄ (R = H, OPh). The result showed that the excited singlet state of phthalocyanine moiety in the D₄-A molecule was efficiently quenched by phenothiazine units owing to intra-molecular PET. The rate constant of PET (k_et) was calculated and the value is much higher than the rate constant of fluorescence emission, intersystem crossing and internal conversion of ZnPc moiety. The laser flash photolysis study revealed the presence of a long-lived charge-separated state due to PET. The results suggest that a D₄-A system can be a more efficient artificial photosynthetic model than D-A towards the practical commercial use.     

Reconstruction for 3D PET Based on Total Variation Constrained Direct Fourier Method

This paper presents a total variation (TV) regularized reconstruction algorithm for 3D positron emission tomography (PET). The proposed method first employs the Fourier rebinning algorithm (FORE), rebinning the 3D data into a stack of ordinary 2D data sets as sinogram data. Then, the resulted 2D sinogram are ready to be reconstructed by conventional 2D reconstruction algorithms. Given the locally piece-wise constant nature of PET images, we introduce the total variation (TV) based reconstruction schemes. More specifically, we formulate the 2D PET reconstruction problem as an optimization problem, whose objective function consists of TV norm of the reconstructed image and the data fidelity term measuring the consistency between the reconstructed image and sinogram. To solve the resulting minimization problem, we apply an efficient methods called the Bregman operator splitting algorithm with variable step size (BOSVS). Experiments based on Monte Carlo simulated data and real data are conducted as validations. The experiment results show that the proposed method produces higher accuracy than conventional direct Fourier (DF) (bias in BOSVS is 70% of ones in DF, variance of BOSVS is 80% of ones in DF).     

Ultra-low dose whole-body CT for attenuation correction in a dual tracer PET/CT protocol for multiple myeloma

PurposeTo investigate within phantoms the minimum CT dose allowed for accurate attenuation correction of PET data and to quantify the effective dose reduction when a CT for this purpose is incorporated in the clinical setting.MethodsThe NEMA image quality phantom was scanned within a large parallelepiped container. Twenty-one different CT images were acquired to correct attenuation of PET raw data. Radiation dose and image quality were evaluated.Thirty-one patients with proven multiple myeloma who underwent a dual tracer PET/CT scan were retrospectively reviewed. ¹⁸F-fluorodeoxyglucose PET/CT included a diagnostic whole-body low dose CT (WBLDCT: 120 kV-80mAs) and ¹¹C-Methionine PET/CT included a whole-body ultra-low dose CT (WBULDCT) for attenuation correction (100 kV-40mAs). Effective dose and image quality were analysed.ResultsOnly the two lowest radiation dose conditions (80 kV-20mAs and 80 kV-10mAs) produced artifacts in CT images that degraded corrected PET images. For all the other conditions (CTDI_vol ≥ 0.43 mGy), PET contrast recovery coefficients varied less than ± 1.2%.Patients received a median dose of 6.4 mSv from diagnostic CT and 2.1 mSv from the attenuation correction CT. Despite the worse image quality of this CT, 94.8% of bone lesions were identifiable.ConclusionPhantom experiments showed that an ultra-low dose CT can be implemented in PET/CT procedures without any noticeable degradation in the attenuation corrected PET scan. The replacement of the standard CT for this ultra-low dose CT in clinical PET/CT scans involves a significant radiation dose reduction.